1. Field of the Invention
The present invention relates to the field of fabricating semiconductor devices, and, in particular, to advanced process control (APC) techniques for manufacturing processes, wherein an improved process control quality is achieved by adjusting process parameters in a predictive manner on the basis of a process model and measurement data.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of semiconductor fabrication, since here it is essential to combine cutting edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve product quality and process tool utilization. The latter aspect is especially important, since in modern semiconductor facilities, equipment is required which is extremely cost-intensive and represents the dominant part of the total product costs. For example, in manufacturing modern integrated circuits, 500 or more individual processes may be necessary to complete the integrated circuit, wherein failure in a single process step may result in a loss of the complete integrated circuit. This problem is even exacerbated in that the size of substrates, on which a plurality of such integrated circuits are processed, steadily increases, so that failure in a single process step may entail the loss of a large number of products.
Therefore, the various manufacturing stages have to be thoroughly monitored to avoid undue waste of man power, tool operation time and raw materials. Ideally, the effect of each individual process step on each substrate would be detected by measurement and the substrate under consideration would be released for further processing only if the required specifications were met. A corresponding process control, however, is not practical, since measuring the effects of certain processes may require relatively long measurement times, frequently ex situ, or may even necessitate the destruction of the sample. Moreover, immense effort, in terms of time and equipment, would have to be made on the metrology side to provide the required measurement results. Additionally, utilization of the process tool would be minimized since the tool would be released only after the provision of the measurement result and its assessment.
The introduction of statistical methods, also referred to as statistical process control (SPC), for adjusting process parameters significantly relaxes the above problem and allows a moderate utilization of the process tools while attaining a relatively high product yield. Statistical process control is based on the monitoring of the process output to thereby identify an out-of-control situation, wherein a causality relationship is established to an external disturbance. After occurrence of an out-of-control situation, usually operator interaction is required to manipulate a process parameter to return to an in-control situation, wherein the causality relationship may be helpful in selecting an appropriate control action. Nevertheless, in total, a large number of dummy substrates or pilot substrates may be necessary to adjust process parameters of respective process tools, wherein tolerable parameter drifts during the process have to be taken into consideration when designing a process sequence, since such parameter drifts may remain undetected over a long time period or may not efficiently be compensated for by SPC techniques.
Recently, a process control strategy has been introduced and is continuously improved allowing a high degree of process control, desirably on a run-to-run basis, with a moderate amount of measurement data. In this control strategy, the so-called advanced process control (APC), a model of a process or of a group of interrelated processes, is established and implemented in an appropriately configured process controller. The process controller also receives information including pre-process measurement data and/or post-process measurement data, as well as information related to, for instance, the substrate history, such as type of process or processes, the product type, the process tool or process tools, in which the products are to be processed or have been processed in previous steps, the process recipe to be used, i.e., a set of required sub-steps for the process or processes under consideration, wherein possibly fixed process parameters and variable process parameters may be contained, and the like. From this information and the process model, the process controller determines a controller state or process state that describes the effect of the process or processes under consideration on the specific product, thereby permitting the establishment of an appropriate parameter setting of the variable parameters of the specified process recipe to be performed with the substrate under consideration.
Thus, the APC controller may have a predictive behavior, which is typically referred to as model predictive control (MPC). Model predictive control schemes, although originally used for real-time control of continuous processes, may also be used for run-to-run control situations in that the continuous time parameter is replaced by a discrete process run index, wherein the controller is now configured to respond to substantially continuous disturbances, also referred to process drifts, and to substantially step-wise disturbances, which may be considered as process shifts. Thus, run-to-run control may provide the potential of compensating for predictable, that is, deterministic, disturbances, such as process shifts and drifts.
One important application of run-to-run control is the monitoring of lithography processes, as the lithography process is one of the most critical processes during the fabrication of semiconductor devices. Moreover, the lithography process may typically provide enhanced control capabilities as the process is typically performed step-wise for each individual substrate, that is, a plurality of individual imaging steps are usually performed for each substrate, thereby enabling individual control of each single step. Consequently, across-wafer uniformity may be controlled by appropriately adapting process parameters of the individual imaging steps. In addition, the lithography has a somewhat unique position in that the process output of the lithography process may be assessed and the lithography process may be repeated when specific process margins are not achieved. On the other hand, lithography is a highly cost-intensive process and undue reprocessing of out-of-control substrates may substantially contribute to overall production costs. One problem, in addition to the appropriate imaging of a mask pattern into a photoresist layer, is the overlay accuracy of lithography processes performed in different device layers. The formation of semiconductor devices and other microstructural features is frequently based on the formation of three-dimensional features by successively forming substantially two-dimensional layers, which have to be precisely aligned to each other so as to provide the final three-dimensional feature having the required characteristics. Consequently, in a lithography process, the image of the reticle used for the current device layer has to be precisely aligned with the previously formed layers. Thus, a plurality of overlay error parameters have been established to allow assessment of the overlay performance including any pre-alignment activities of the lithography tools.
FIGS. 1a and 1b schematically illustrate eight overlay error parameters that may typically be used as control variables in a run-to-run controller for substantially maintaining the overlay parameters on target. FIG. 1a schematically shows a substrate 150 having formed thereon a first pattern of features 151 and a second pattern of features 152 that has been formed by lithography, wherein inspection of the patterns 151 and 152 may allow establishment of numerical values of overlay error parameters, such as magnification, x-scale, y-scale, substrate rotation and orthogonality. Moreover, FIG. 1b schematically shows overlay error parameters related to a reticle rotation and translations in the x- and y-directions. Consequently, a corresponding exposure tool recipe may contain eight manipulated variables that correspond to the eight overlay error parameters specified above. Hereby, the manipulated variables may represent so-called controller inputs, that is, any process parameters of the lithography tool which may be adjusted by the controller so as to obtain specified values for the above-specified overlay error parameters or control variables, such as magnification, x-translation, orthogonality and the like. Frequently, the lithography tools are designed such that a linear model may be used to correlate the detected overlay error parameters or control variables to the respective manipulated variables.
Equation 1 illustrates a corresponding linear model, in which the process gain, i.e., the slope of the straight line represented by the linear model, is selected to 1, wherein Ek represents one of the overlay error parameters, Ck represents the associated manipulated variable and Ik represents the intercept.Ek=Ck+Ik  (1)
In order to calculate the optimal process input, i.e., the respective values for the manipulated variables Ck, it is typically assumed that the corresponding intercept Ik is only constant in a local sense, since process drifts and shifts may occur over time due to tool aging and/or process disturbances. Thus, based on the above model and the non-constancy of the various intercepts Ik, appropriate values of the manipulated variables may be calculated on the basis of well-established controller schemes, thereby significantly reducing the effect of drifts and disturbances on the overlay error parameters Ek. In order to keep the alignment characteristics at substrate level substantially constant, any tool drifts are usually compensated for by calibration of set points of the manipulated variables by means of machine constants, which are verified during preventive maintenance on a regular basis. In this connection, it should be appreciated that the term “machine constants” does not necessarily mean that the “constants” are stable over time. Rather, the machine constants are affected by the tool drifts and the corresponding value drift is “monitored” by the preventive maintenance operations. For example, any reference positions for the x- and y-translations may represent machine constants based on which corresponding manipulated variables, such as a control signal for a corresponding x- and y-drive motor, may be adjusted. Consequently, any changes in the machine constants will directly result in a change of the tool state and thus will lead to an offset of the respective alignment parameter intercepts Ik. Depending on the variation of the corresponding machine constants revealed by a corresponding preventive maintenance activity, conventionally different control strategies may be performed. When a variation of the machine constant occurs in moderately little steps, it is assumed that the controller may react to these small “step disturbances” in an appropriate manner and no further activity is required. In addition or alternatively, the controller data produced so far may be discarded, i.e., the controller, at least for the specified process tool and the respective process recipe, may be reset, thereby requiring a new initialization at least for all controller data referring to the specified process tool and the corresponding process recipe, thereby necessitating the processing of pilot substrates. Thus, irrespective of the control scheme used, a reduced controller performance may be obtained owing to the changes of machine constants, since the occurrence of step disturbances, introduced by the updated machine constants which directly affect the set point calibration and thus the controller performance, may result in declined controller performance immediately after the disturbance, while resetting of the controller may result in reduced throughput and compromised controller performance at an initial phase.
In view of the situation described above, there exists a need for an enhanced technique that enables an enhanced control strategy, wherein one or more of the problems identified above may be avoided or the effects thereof at least be significantly reduced.